Tag: Clustering

Calib v0.3.6 – Clustering UMI-barcoded Sequencing data

Calib v0.3.6

:: DESCRIPTION

Calib clusters barcode tagged paired-end reads based on their barcode and sequence similarity.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • conda
  • Python

:: DOWNLOAD

Calib

:: MORE INFORMATION

Citation

Orabi B, Erhan E, McConeghy B, Volik SV, Le Bihan S, Bell R, Collins CC, Chauve C, Hach F.
Alignment-free clustering of UMI tagged DNA molecules.
Bioinformatics. 2019 Jun 1;35(11):1829-1836. doi: 10.1093/bioinformatics/bty888. PMID: 30351359.

PathoGiST v0.3.6 – Clustering Pathogen Isolates by combining multiple Genotyping Signals

PathoGiST v0.3.6

:: DESCRIPTION

PathOGiST is an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • conda

:: DOWNLOAD

PathOGiST

:: MORE INFORMATION

Citation

Katebi M. et al. (2020)
PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals.
Algorithms for Computational Biology. AlCoB 2020. Lecture Notes in Computer Science, vol 12099. Springer, Cham. https://doi.org/10.1007/978-3-030-42266-0_9

McKmeans 0.42 – Multi-core algorithm for Clustering extremely large datasets

McKmeans 0.42

:: DESCRIPTION

McKmeans (multi-core parallel cluster algorithm) is highly efficient multi-core k-means algorithm for clustering extremely large datasets.

::DEVELOPER

Medical Systems Biology, University of Ulm

:: SCREENSHOTS

McKmeans

:: REQUIREMENTS

  • Linux/ Mac OsX/ Windows
  • Java/ R package

:: DOWNLOAD

 McKmeans

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Apr 6;11:169. doi: 10.1186/1471-2105-11-169.
A highly efficient multi-core algorithm for clustering extremely large datasets.
Kraus JM, Kestler HA.

DBC 6.11.13 / dbOTU3 – Distribution-based Clustering

DBC 6.11.13 / dbOTU3

:: DESCRIPTION

The DBC software is an alternative method for organizing sequence data into operational taxonomic units (OTUs) for next-generation sequencing technologies, such as Illumina. The focus of this method is to identify sequences that are genetically and ecologically similar to group them, while keeping ecologically distinct organisms apart, regardless of sequence identity.

dbOTU3 is a new implementation of dbOTU that is faster and more user-friendly.

::DEVELOPER

The Alm lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl
  • R

:: DOWNLOAD

DBC  / dbOTU3

:: MORE INFORMATION

Citation

Olesen SW, Duvallet C, Alm EJ.
dbOTU3: A new implementation of distribution-based OTU calling.
PLoS One. 2017 May 4;12(5):e0176335. doi: 10.1371/journal.pone.0176335. PMID: 28472072; PMCID: PMC5417438.

Sarah P. Preheim, Allison R. Perrotta, Antonio M. Martin-Platero, Anika Gupta and Eric J. Alm. 2013.
Distribution-based clustering: Using ecology to refine the operational taxonomic unit.
Appl. Environ. Microbiol. 2013, 79(21):659

BnpC – Bayesian non-parametric Clustering of Single-cell Mutation Profiles

BnpC

:: DESCRIPTION

BnpC is a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles.

::DEVELOPER

Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • Python

:: DOWNLOAD

BnpC

:: MORE INFORMATION

Citation:

Borgsmüller N, Bonet J, Marass F, Gonzalez-Perez A, Lopez-Bigas N, Beerenwinkel N.
BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.
Bioinformatics. 2020 Dec 8;36(19):4854-4859. doi: 10.1093/bioinformatics/btaa599. PMID: 32592465; PMCID: PMC7750970.

NB.MClust 1.1.1 – Negative Binomial Model-Based Clustering

NB.MClust 1.1.1

:: DESCRIPTION

NB.Mclust is an R package for Negative binomial model-based clustering.

::DEVELOPER

Fridley Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • R

:: DOWNLOAD

NB.Mclust

:: MORE INFORMATION

Citation:

Li Q, Noel-MacDonnell JR, Koestler DC, Goode EL, Fridley BL.
Subject level clustering using a negative binomial model for small transcriptomic studies.
BMC Bioinformatics. 2018 Dec 12;19(1):474. doi: 10.1186/s12859-018-2556-9. PMID: 30541426; PMCID: PMC6292049.

LUMIWCLUSTER 1.0.2 – Implement Weighted Model based Clustering

LUMIWCLUSTER 1.0.2

:: DESCRIPTION

LUMIWCLUSTER is an R package that implements a weighted model based clustering for Illumina BeadArray Methylation Assays.

::DEVELOPER

Pei Fen Kuan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  LUMIWCLUSTER

:: MORE INFORMATION

Citation

Kuan, P., Wang, S., Zhou, X., and Chu, H. (2010).
A statistical framework for Illumina DNA methylation array.
Bioinformatics, 26 (22): 2849-2855.

hclust 1.0 – Clustering Expression data with Hopfield Networks

hclust 1.0

:: DESCRIPTION

hclust demonstrates the usage of Hopfield networks for clustering, feature selection and network inference.

::DEVELOPER

hclust team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Python
  • matplotlib

:: DOWNLOAD

 hclust

:: MORE INFORMATION

Citation

Bioinformatics. 2014 May 1;30(9):1273-9. doi: 10.1093/bioinformatics/btt773. Epub 2014 Jan 8.
Characterizing cancer subtypes as attractors of Hopfield networks.
Maetschke SR1, Ragan MA.

EFC – Evolutionary Fuzzy Clustering

EFC

:: DESCRIPTION

EFC (Evolutionary Fuzzy Clustering) is able to deal with overlapping clusterings.

::DEVELOPER

The Centre for Integrative Bioinformatics VU

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

EFC Source Code

:: MORE INFORMATION

Citation

Van Houte, B.P.P. and Heringa, J. (2010).
Accurate confidence aware clustering of array CGH tumor profiles.
Bioinformatics, 26(1): 6-14

CLIC v1.0 – Clustering by Inferred Co-expression

CLIC v1.0

:: DESCRIPTION

CLIC is a computational tool for helping users identify new members of a pathway of interest, as well as the RNA expression datasets in which that pathway is relevant.

::DEVELOPER

Jun Liu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOS

:: DOWNLOAD

CLIC

:: MORE INFORMATION

Citation

Li Y, Jourdain AA, Calvo SE, Liu JS, Mootha VK.
CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets.
PLoS Comput Biol. 2017 Jul 18;13(7):e1005653. doi: 10.1371/journal.pcbi.1005653. PMID: 28719601; PMCID: PMC5546725.

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